The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

Blockchain systems are lauded for their security and reliability. Security is a cornerstone, as they employ cryptographic techniques to ensure the immutability of data, making it extremely resistant to tampering. With decentralized networks, they also reduce the risk of a single point of failure, enhancing reliability. Model checking plays a vital role in ensuring the security and reliability of blockchain systems. However, traditional model-checking approaches face challenges in handling the inherent dynamism exhibited in blockchain systems. To overcome this challenge, Aspect-Oriented programming (AOP) offers capabilities to enhance blockchain model checking through the modularization of cross-cutting concerns, enabling traceability and monitoring, facilitating dynamic instrumentation, and supporting fine-grained property specifications. The aim of this research is to enable more effective and efficient verification of dynamic behaviors in blockchain systems compared to conventional model-checking techniques using AOP. As a result, this research introduces BlockASP , a novel blockchain model verification method that leverages AOP to analyze and monitor dynamic behavior of the blockchain system. BlockASP integrates the benefits of aspect-orientation and model checking into the blockchain architecture to strengthen security and reliability. This research has examined prior arts that are related to blockchain modeling using Object-oriented (OO) and those that are using AOP. Our research has proposed and discussed the BlockASP technique, the research provided a case study to demonstrate the validity and superiority in facilitating the monitoring of dynamic blockchain behavior using AOP compared to traditional approaches such as Model-Driven Architecture (MDA).

Modernizing legacy software has always been a core concern of businesses and industries that strive to improve their performance. However, decision makers fail to address each criterion’s concerns and provide precise solutions for how legacy software modernization can be implemented. This paper proposes a new Multi-Criteria Decision-Making (MCDM) method for legacy software modernization. The aim of the proposed method is to rank different software modernization solutions based on the predetermined evaluation criteria. The evaluation criteria considered in this work include Motivating Factors and Challenges criteria that possibly affect legacy software modernization. The method introduced is a combination of the Full Consistency Method (FUCOM) and Weighted Sum Method (WSM), in which FUCOM is used to maintain the reliability of the decision model and WSM to calculate overall importance or weight of each criterion in the decision-making process. This MCDM method is formulated to obtain the most optimal solution that satisfies the evaluation criteria defined by industrial stakeholders based on the survey conducted. A case study on modernizing legacy billing software for a telecommunication company is used to further illustrate the implementation of the proposed methodology in different situations.

Federated learning (FL) has emerged as a paradigm-shifting approach for collaborative machine learning (ML) while preserving data privacy. However, existing FL frameworks face significant challenges in ensuring trustworthiness, regulatory compliance, and security across heterogeneous institutional environments. We introduce AspectFL, a novel aspect-oriented programming (AOP) framework that seamlessly integrates trust, compliance, and security concerns into FL systems through cross-cutting aspect weaving. Our framework implements four core aspects: FAIR (Findability, Accessibility, Interoperability, Reusability) compliance, security threat detection and mitigation, provenance tracking, and institutional policy enforcement. AspectFL employs a sophisticated aspect weaver that intercepts FL execution at critical joinpoints, enabling dynamic policy enforcement and real-time compliance monitoring without modifying core learning algorithms. We demonstrate AspectFL’s effectiveness through experiments on healthcare and financial datasets, including a detailed and reproducible evaluation on the real-world MIMIC-III dataset. Our results, reported with 95% confidence intervals and validated with appropriate statistical tests, show significant improvements in model performance, with a 4.52% and 0.90% increase in Area Under the Curve (AUC) for the healthcare and financial scenarios, respectively. Furthermore, we present a detailed ablation study, a comparative benchmark against existing FL frameworks, and an empirical scalability analysis, demonstrating the practical viability of our approach. AspectFL achieves high FAIR compliance scores (0.762), robust security (0.798 security score), and consistent policy adherence (over 84%), establishing a new standard for trustworthy FL.

In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have sought to integrate technology, especially business intelligence, into their operations to ensure their achievement. This study aims to provide a systematic literature review of the intersection of information systems and sustainability in business intelligence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilized to select high-quality studies from various databases, including ScienceDirect, IEEE Xplore, and Scopus, to be included in this review. The methodology resulted in 32 studies taxonomized into four main categories covering different aspects of the intersection of information systems and sustainability. This study discusses integrating information systems and sustainability in various sectors, such as tourism, health, urban, and other sectors, with different technologies, such as Blockchain, IoT, Industry 4.0, and other innovations. Moreover, the information system types implemented to support sustainability practices in different domains are highlighted.